An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation.

ABSTRACT: Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. In this report, we leverage a large database of obstetric US images acquired, stored and annotated by expert sonographers to train algorithms to classify, segment, and measure several fetal structures: biparietal diameter (BPD), head circumference (HC), crown rump length (CRL), abdominal circumference (AC), and femur length (FL). We present a technique for generating raw images suitable for model training by removing caliper a

SUBMITTER: Prieto JC 

PROVIDER: S-EPMC8086527 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature